In [1]:
import pandas as pd
In [3]:
data_assign = pd.read_csv("Car Price.csv")
In [5]:
data_assign
Out[5]:
| Brand | Model | Year | Selling_Price | KM_Driven | Fuel | Seller_Type | Transmission | Owner | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | Maruti | Maruti 800 AC | 2007 | 60000 | 70000 | Petrol | Individual | Manual | First Owner |
| 1 | Maruti | Maruti Wagon R LXI Minor | 2007 | 135000 | 50000 | Petrol | Individual | Manual | First Owner |
| 2 | Hyundai | Hyundai Verna 1.6 SX | 2012 | 600000 | 100000 | Diesel | Individual | Manual | First Owner |
| 3 | Datsun | Datsun RediGO T Option | 2017 | 250000 | 46000 | Petrol | Individual | Manual | First Owner |
| 4 | Honda | Honda Amaze VX i-DTEC | 2014 | 450000 | 141000 | Diesel | Individual | Manual | Second Owner |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 4335 | Hyundai | Hyundai i20 Magna 1.4 CRDi (Diesel) | 2014 | 409999 | 80000 | Diesel | Individual | Manual | Second Owner |
| 4336 | Hyundai | Hyundai i20 Magna 1.4 CRDi | 2014 | 409999 | 80000 | Diesel | Individual | Manual | Second Owner |
| 4337 | Maruti | Maruti 800 AC BSIII | 2009 | 110000 | 83000 | Petrol | Individual | Manual | Second Owner |
| 4338 | Hyundai | Hyundai Creta 1.6 CRDi SX Option | 2016 | 865000 | 90000 | Diesel | Individual | Manual | First Owner |
| 4339 | Renault | Renault KWID RXT | 2016 | 225000 | 40000 | Petrol | Individual | Manual | First Owner |
4340 rows × 9 columns
In [7]:
data_assign.head()
Out[7]:
| Brand | Model | Year | Selling_Price | KM_Driven | Fuel | Seller_Type | Transmission | Owner | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | Maruti | Maruti 800 AC | 2007 | 60000 | 70000 | Petrol | Individual | Manual | First Owner |
| 1 | Maruti | Maruti Wagon R LXI Minor | 2007 | 135000 | 50000 | Petrol | Individual | Manual | First Owner |
| 2 | Hyundai | Hyundai Verna 1.6 SX | 2012 | 600000 | 100000 | Diesel | Individual | Manual | First Owner |
| 3 | Datsun | Datsun RediGO T Option | 2017 | 250000 | 46000 | Petrol | Individual | Manual | First Owner |
| 4 | Honda | Honda Amaze VX i-DTEC | 2014 | 450000 | 141000 | Diesel | Individual | Manual | Second Owner |
In [9]:
data_assign.describe()
Out[9]:
| Year | Selling_Price | KM_Driven | |
|---|---|---|---|
| count | 4340.000000 | 4.340000e+03 | 4340.000000 |
| mean | 2013.090783 | 5.041273e+05 | 66215.777419 |
| std | 4.215344 | 5.785487e+05 | 46644.102194 |
| min | 1992.000000 | 2.000000e+04 | 1.000000 |
| 25% | 2011.000000 | 2.087498e+05 | 35000.000000 |
| 50% | 2014.000000 | 3.500000e+05 | 60000.000000 |
| 75% | 2016.000000 | 6.000000e+05 | 90000.000000 |
| max | 2020.000000 | 8.900000e+06 | 806599.000000 |
In [11]:
data_assign.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 4340 entries, 0 to 4339 Data columns (total 9 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Brand 4340 non-null object 1 Model 4340 non-null object 2 Year 4340 non-null int64 3 Selling_Price 4340 non-null int64 4 KM_Driven 4340 non-null int64 5 Fuel 4340 non-null object 6 Seller_Type 4340 non-null object 7 Transmission 4340 non-null object 8 Owner 4340 non-null object dtypes: int64(3), object(6) memory usage: 305.3+ KB
In [13]:
data_assign.columns
Out[13]:
Index(['Brand', 'Model', 'Year', 'Selling_Price', 'KM_Driven', 'Fuel',
'Seller_Type', 'Transmission', 'Owner'],
dtype='object')
In [15]:
data_assign.columns.tolist()
Out[15]:
['Brand', 'Model', 'Year', 'Selling_Price', 'KM_Driven', 'Fuel', 'Seller_Type', 'Transmission', 'Owner']
In [17]:
Unique_data = {}
for column in data_assign.columns:
Unique_data[column] = data_assign[column].nunique()
In [19]:
Unique_data
Out[19]:
{'Brand': 29,
'Model': 1491,
'Year': 27,
'Selling_Price': 445,
'KM_Driven': 770,
'Fuel': 5,
'Seller_Type': 3,
'Transmission': 2,
'Owner': 5}
Linear Regressions
In [21]:
X = data_assign[['KM_Driven']]
y = data_assign['Selling_Price']
In [23]:
from sklearn.model_selection import train_test_split
In [25]:
X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.2, random_state=42)
In [27]:
from sklearn.linear_model import LinearRegression
In [29]:
model = LinearRegression()
model
Out[29]:
LinearRegression()In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
LinearRegression()
In [31]:
model.fit(X_train, y_train)
Out[31]:
LinearRegression()In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
LinearRegression()
In [33]:
lin_pred = model.predict(X_test)
In [35]:
lin_pred
Out[35]:
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329389.3674441 , 403743.27079086, 380093.81043884,
604763.68378304, 415568.00096687, 569289.49325501,
514895.73444536, 451042.1914949 , 616588.41395905,
661498.73916754, 566924.54721981, 190898.12762266,
521990.57255097, 592938.95360703, 403743.27079086,
479421.54391733, 604763.68378304, 592938.95360703,
427392.73114288, 521990.57255097, 380093.81043884,
628413.14413506, 581114.22343102, 661522.38862789,
474691.65184692, 498341.11219894, 581114.22343102,
533815.30272698, 569289.49325501, 451042.1914949 ,
545640.03290299, 521990.57255097, 552734.87100859,
548004.97893819, 644967.76638148, 526720.46462137,
498341.11219894, 474691.65184692, 380093.81043884,
557464.763079 , 491246.27409334, 582533.19105214,
605563.03554294, 474691.65184692, 578749.27739582,
640237.87431107, 332794.88973479, 643785.29336388,
332794.88973479, 427392.73114288, 403743.27079086,
344619.6199108 , 545640.03290299, 611858.52188865,
521990.57255097, 585844.11550142, 501888.53125175,
529085.41065657, 380093.81043884, 616588.41395905,
595303.89964223, 295343.60432133, 537126.22717626,
525537.99160377, 503071.00426935, 498341.11219894,
613291.67918598, 581114.22343102, 414553.43911777,
607128.62981824, 604763.68378304, 537003.24998243,
380093.81043884, 597730.33427435, 451042.1914949 ,
380093.81043884, 408473.16286126, 525537.99160377,
521990.57255097, 261846.50867873, 590574.00757183,
481786.48995253, 578749.27739582, 604763.68378304,
479421.54391733, 380093.81043884, 550369.92497339,
380093.81043884, 521990.57255097, 521990.57255097,
451042.1914949 , 652062.60448708, 451042.1914949 ,
474691.65184692, 486516.38202293, 661049.39942085,
640237.87431107, 597668.84567744, 498341.11219894,
581114.22343102, 569289.49325501, 403743.27079086,
628413.14413506, 519429.33599484, 403743.27079086,
621318.30602946, 427392.73114288, 521990.57255097,
475540.66747356, 474691.65184692, 552351.74975089,
190898.12762266, 427392.73114288, 521990.57255097,
380093.81043884, 569289.49325501, 648562.48435499,
592938.95360703])
In [37]:
from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
In [39]:
mean_absolute_error(y_test, lin_pred)
Out[39]:
300629.95639712235
In [41]:
mean_squared_error(y_test, lin_pred)
Out[41]:
292829552069.2159
In [43]:
r2_score(y_test, lin_pred)
Out[43]:
0.04043884538588105
In [45]:
import matplotlib.pyplot as plt
In [47]:
plt.figure()
plt.scatter(y_test, lin_pred)
plt.xlabel("Actual Selling Price")
plt.ylabel("Predicted Selling Price")
plt.title("Actual vs Predicted Selling Price")
plt.show()
In [ ]:
In [ ]:
Decision Tree Regressions
In [50]:
from sklearn.preprocessing import LabelEncoder
In [52]:
fuel_encoder = LabelEncoder()
data_assign["Fuel_Encoded"] = fuel_encoder.fit_transform(data_assign["Fuel"])
In [54]:
X = data_assign[['Fuel_Encoded']]
y = data_assign['Selling_Price']
In [56]:
from sklearn.model_selection import train_test_split
In [58]:
X_train, X_test, y_train, y_test = train_test_split(X,y, test_size= 0.2, random_state=42)
In [60]:
from sklearn.tree import DecisionTreeClassifier
In [62]:
model_tree = DecisionTreeClassifier()
In [64]:
model_tree
Out[64]:
DecisionTreeClassifier()In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
DecisionTreeClassifier()
In [66]:
model_tree.fit(X_train, y_train)
Out[66]:
DecisionTreeClassifier()In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
DecisionTreeClassifier()
In [68]:
y_pred = model_tree.predict(X_test)
In [70]:
y_pred
Out[70]:
array([450000, 300000, 300000, 450000, 450000, 300000, 300000, 300000,
450000, 300000, 450000, 450000, 450000, 450000, 300000, 300000,
450000, 450000, 300000, 450000, 450000, 450000, 300000, 450000,
450000, 450000, 450000, 300000, 300000, 300000, 300000, 300000,
450000, 450000, 300000, 450000, 450000, 300000, 300000, 300000,
450000, 300000, 450000, 450000, 450000, 450000, 300000, 450000,
450000, 450000, 450000, 300000, 300000, 300000, 300000, 450000,
450000, 450000, 300000, 300000, 300000, 450000, 300000, 450000,
300000, 300000, 300000, 300000, 450000, 450000, 450000, 300000,
300000, 450000, 450000, 450000, 450000, 450000, 300000, 450000,
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300000, 300000, 300000, 300000, 300000, 300000, 300000, 450000,
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300000, 300000, 450000, 450000, 450000, 450000, 450000, 450000,
300000, 450000, 450000, 450000, 300000, 300000, 450000, 450000,
300000, 300000, 300000, 300000, 300000, 300000, 300000, 450000,
300000, 300000, 300000, 450000, 450000, 300000, 300000, 450000,
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300000, 450000, 450000, 450000, 300000, 300000, 300000, 450000,
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300000, 450000, 450000, 300000, 300000, 300000, 300000, 450000,
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450000, 300000, 450000, 450000, 450000, 365000, 450000, 300000,
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300000, 450000, 300000, 300000, 450000, 450000, 450000, 450000,
300000, 300000, 450000, 300000, 450000, 450000, 450000, 450000,
300000, 450000, 300000, 450000, 300000, 300000, 300000, 450000,
450000, 450000, 450000, 300000, 300000, 450000, 300000, 300000,
300000, 450000, 450000, 300000, 450000, 300000, 450000, 300000,
300000, 450000, 300000, 450000, 450000, 300000, 450000, 450000,
80000, 450000, 300000, 450000, 300000, 450000, 300000, 450000,
80000, 450000, 300000, 450000, 300000, 300000, 300000, 450000,
300000, 300000, 450000, 300000, 300000, 300000, 450000, 450000,
300000, 450000, 450000, 450000, 300000, 300000, 450000, 450000,
365000, 450000, 450000, 450000, 450000, 300000, 450000, 300000,
300000, 450000, 300000, 300000, 450000, 300000, 450000, 450000,
450000, 300000, 450000, 300000, 450000, 450000, 450000, 300000,
365000, 300000, 300000, 450000, 300000, 450000, 450000, 300000,
450000, 300000, 450000, 300000, 450000, 450000, 450000, 450000,
450000, 450000, 300000, 450000, 300000, 450000, 300000, 300000,
300000, 450000, 450000, 300000, 300000, 450000, 450000, 300000,
450000, 450000, 300000, 300000, 450000, 300000, 450000, 300000,
300000, 300000, 450000, 300000, 450000, 300000, 450000, 450000,
300000, 450000, 300000, 300000, 300000, 450000, 450000, 450000,
450000, 300000, 300000, 450000, 450000, 450000, 450000, 450000,
300000, 300000, 300000, 300000, 300000, 300000, 300000, 450000,
300000, 300000, 300000, 450000, 450000, 300000, 450000, 300000,
300000, 300000, 300000, 300000, 450000, 300000, 450000, 450000,
450000, 450000, 300000, 300000, 450000, 450000, 450000, 450000,
300000, 450000, 300000, 300000, 300000, 450000, 450000, 450000,
450000, 300000, 450000, 365000, 450000, 80000, 450000, 300000,
450000, 300000, 300000, 450000, 300000, 450000, 300000, 300000,
450000, 300000, 300000, 450000, 450000, 300000, 300000, 300000,
300000, 450000, 450000, 450000, 450000, 300000, 300000, 450000,
300000, 300000, 300000, 450000, 300000, 300000, 300000, 300000,
300000, 300000, 300000, 300000, 300000, 450000, 300000, 300000,
300000, 450000, 300000, 300000, 300000, 450000, 300000, 450000,
300000, 300000, 450000, 450000, 450000, 450000, 300000, 450000,
450000, 450000, 300000, 300000, 450000, 450000, 450000, 450000,
300000, 450000, 300000, 450000, 300000, 450000, 450000, 450000,
300000, 450000, 300000, 300000, 450000, 450000, 450000, 450000,
450000, 300000, 450000, 450000, 450000, 300000, 300000, 300000,
300000, 450000, 300000, 450000, 450000, 450000, 300000, 450000,
300000, 300000, 450000, 300000, 450000, 300000, 300000, 450000,
300000, 300000, 300000, 450000, 300000, 300000, 450000, 450000,
300000, 300000, 450000, 300000, 450000, 300000, 300000, 450000,
450000, 450000, 450000, 450000, 300000, 450000, 450000, 300000,
300000, 300000, 300000, 300000, 450000, 450000, 300000, 300000,
300000, 450000, 450000, 300000, 450000, 300000, 300000, 300000,
300000, 300000, 300000, 300000, 450000, 300000, 300000, 450000,
300000, 450000, 300000, 450000, 300000, 300000, 300000, 300000,
450000, 450000, 300000, 300000, 450000, 300000, 300000, 450000,
300000, 300000, 450000, 450000, 450000, 450000, 300000, 300000,
450000, 300000, 300000, 450000, 450000, 450000, 300000, 300000,
450000, 450000, 450000, 450000, 300000, 450000, 450000, 450000,
300000, 300000, 450000, 450000, 300000, 450000, 450000, 300000,
300000, 300000, 300000, 450000, 450000, 300000, 450000, 300000,
450000, 450000, 300000, 300000, 450000, 450000, 300000, 300000,
450000, 300000, 450000, 300000, 450000, 450000, 300000, 300000,
450000, 450000, 300000, 300000], dtype=int64)
In [72]:
mean_absolute_error(y_test, y_pred)
Out[72]:
273683.28686635947
In [74]:
mean_squared_error(y_test, y_pred)
Out[74]:
302642379882.89056
In [76]:
r2_score(y_test, y_pred)
Out[76]:
0.008283592200596268
In [78]:
from sklearn.tree import plot_tree
import matplotlib.pyplot as plt
In [80]:
plot_tree(model_tree);
In [ ]:
In [ ]:
In [87]:
plt.figure(figsize=(40,20), dpi=800)
plot_tree(
model_tree,
feature_names=["Fuel_Encoded"],
filled=True,
rounded=True,
fontsize=9
)
plt.savefig('Course_work_image')
plt.show()
